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Daniel Han-Chen 2024-02-01 22:21:35 +11:00
commit 24c4c37b7c
2 changed files with 21 additions and 21 deletions
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2
unsloth/kernels/utils.py
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@ -192,7 +192,7 @@ def fast_linear_forward(proj, X, temp_lora = None, out = None):
bsz, _, in_dim = X.shape
if W_quant is None:
out = torch.matmul(X, W.t())
out = torch.matmul(X, W.t(), out = out)
elif bsz <= 4:
# Only batches of 4 are faster with Gemv
out = fast_gemv(X, W, W_quant, out = out)

36
unsloth/models/llama.py
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@ -235,28 +235,28 @@ def LlamaAttention_fast_forward_inference(
temp_QA = torch.empty((2, bsz, 1, hd), dtype = dtype, device = "cuda")
temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = "cuda")
Qn = fast_linear_forward(self.q_proj, Xn, out = temp_QA[0])
Kn = fast_linear_forward(self.k_proj, Xn, out = temp_KV[0])
Vn = fast_linear_forward(self.v_proj, Xn, out = temp_KV[1])
Qn = fast_linear_forward(self.q_proj, Xn)#, out = temp_QA[0])
Kn = fast_linear_forward(self.k_proj, Xn)#, out = temp_KV[0])
Vn = fast_linear_forward(self.v_proj, Xn)#, out = temp_KV[1])
Qn = Qn.view(bsz, 1, n_heads, head_dim).transpose(1, 2)
Kn = Kn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)
Vn = Vn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)
seq_len = K1.shape[-2]
kv_seq_len = seq_len + 1
# cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len)
# Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids)
cos = self.rotary_emb.cos_cached[seq_len]
sin = self.rotary_emb.sin_cached[seq_len]
h = head_dim // 2
cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len)
Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids)
# cos = self.rotary_emb.cos_cached[seq_len]
# sin = self.rotary_emb.sin_cached[seq_len]
# h = head_dim // 2
RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = Xn.dtype, device = "cuda")
RH_Q[:,:,:,:h] = Qn[:,:,:,h:]; RH_Q[:,:,:,h:] = Qn[:,:,:,:h]; torch.neg(RH_Q[:,:,:,:h], out = RH_Q[:,:,:,:h]);
Qn *= cos; Qn.addcmul_(RH_Q, sin);
# RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = Xn.dtype, device = "cuda")
# RH_Q[:,:,:,:h] = Qn[:,:,:,h:]; RH_Q[:,:,:,h:] = Qn[:,:,:,:h]; torch.neg(RH_Q[:,:,:,:h], out = RH_Q[:,:,:,:h]);
# Qn *= cos; Qn.addcmul_(RH_Q, sin);
RH_K = RH_Q[:,:n_kv_heads,:,:] # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = "cuda")
RH_K[:,:,:,:h] = Kn[:,:,:,h:]; RH_K[:,:,:,h:] = Kn[:,:,:,:h]; torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h]);
Kn *= cos; Kn.addcmul_(RH_K, sin);
# RH_K = RH_Q[:,:n_kv_heads,:,:] # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = "cuda")
# RH_K[:,:,:,:h] = Kn[:,:,:,h:]; RH_K[:,:,:,h:] = Kn[:,:,:,:h]; torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h]);
# Kn *= cos; Kn.addcmul_(RH_K, sin);
# New KV cache
Kn = torch.cat([K1, Kn], dim = 2)
@ -279,7 +279,7 @@ def LlamaAttention_fast_forward_inference(
A = torch.matmul(A, Vnn, out = Qn)
A = A.transpose(1, 2)
A = A.reshape(bsz, 1, self.hidden_size)
A = fast_linear_forward(self.o_proj, A, out = temp_QA[1])
A = fast_linear_forward(self.o_proj, A)#, out = temp_QA[1])
return A, (Kn, Vn)
pass
@ -291,13 +291,13 @@ def fast_mlp_inference(self, X):
mlp_size = self.config.intermediate_size
temp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = "cuda")
gate = fast_linear_forward(self.gate_proj, X, out = temp[0])
up = fast_linear_forward(self. up_proj, X, out = temp[1])
gate = fast_linear_forward(self.gate_proj, X)#, out = temp[0])
up = fast_linear_forward(self. up_proj, X)#, out = temp[1])
gate = torch.nn.functional.silu(gate, inplace = True)
gate *= up
# X = self.down_proj(gate)
down = fast_linear_forward(self.down_proj, gate, out = up[:,:,:hd])
down = fast_linear_forward(self.down_proj, gate)#, out = up[:,:,:hd])
return down
pass